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1 Gesture recognition Using HMMs and size functions
2 Approach Combination of HMMs (for dynamics) and size functions (for pose representation)
3 Size functions Topological representation of contours
4 Measuring functions Functions on the contour to which the size function is computed real image measuring function family of lines
5 Feature extraction 1 An edge map is extracted from the image real imageedge map … and …
6 Feature extraction 2 a family of measuring functions is chosen … the szfc are computed, and their means form the feature vector
7 Hidden Markov models Finite-state model of gestures as sequences of a small number of poses
8 Four-state HMM Gesture dynamics -> transition matrix A Object poses -> state-output matrix C
9 EM algorithm feature matrices: collection of feature vectors along time EM A,C learning the models parameters through EM two instances of the same gesture
10 EM algorithm -> learning the models parameters Learning algorithm
11 Gesture classification … HMM 1 HMM 2 HMM n the new sequence is fed to the learnt gestures models they produce a likelihood the most likely model is chosen (if above a threshold)
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